In traditional warehousing, physically separate warehouses may exist that store regionalized or logically separate data. When a user wants to know what data exists in warehouses other than the user's local one, the user must issue separate queries to each warehouse and get results back separately. This creates a labor-intensive “hit-or-miss” guess as to where the data may be located. For example, a user does not typically know if a suspect exists in any particular warehouse, so the user must issue a query to each warehouse and wait for the database to return results or a message that nothing is found. This is extremely inefficient and time- and labor-intensive. Even if the warehouse application can automatically issue queries to the remote systems, it still must wait for the responses from each system to know if data exists.
Metadata servers and Pointer systems have been proposed, which store pointers to data that resides in disparate systems. The problem with metadata servers is that none of the detailed source data resides on the metadata server and additional queries must still be issued to the remote system in order to get any meaningful information. Additionally, metadata servers do not consider the consolidation of objects across the various sources that they point to. This means that potential relevant data may be missed because the metadata pointer system is not smart enough to know that the data is related. In other words, metadata servers cannot tell a user that entity 1 consolidates with entity 2 and has additional relationships with entity 3.
Other proposed solutions might try to employ statistical or probabilistic algorithms to cause a query system to make educated guesses to improve query efficiencies; however, such systems inherently incorporate a level of unreliability or non-confidence in the results. For example, the following court exchange can be envisioned:                Lawyer: “So, Officer, how do you know that the suspect was the same person that was in your records system?”        Officer: “Because the system told me that the suspect had a 73.27 percent chance of being the same person . . . ”        
Law enforcement, as well as many other industries, need accurate, actionable information—not mathematical probabilities—in addition to a system that can deliver information efficiently.